Chicago Traffic Accident Map_2025¶

In [1]:
#  Install required libraries (run once)
!pip install osmnx geopandas folium branca pandas

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In [2]:
#Import libraries
import os
import pandas as pd
import geopandas as gpd
import osmnx as ox
import folium
import branca.colormap as cm
In [3]:
#Adding chicago traffic csv data which I downloded from Chicago govt website

crashes = pd.read_csv("Traffic_Crashes_20251115.csv")

# Converting crash records to GeoDataFrame
crash_gdf = gpd.GeoDataFrame(
    crashes,
    geometry=gpd.points_from_xy(crashes["LONGITUDE"], crashes["LATITUDE"]),
    crs="EPSG:4326"
)


crash_gdf.head()

/var/folders/tn/qthxn6q95rd17mg5gsz5ctjh0000gq/T/ipykernel_9571/3258216460.py:3: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.
  crashes = pd.read_csv("Traffic_Crashes_20251115.csv")
Out[3]:
CRASH_RECORD_ID CRASH_DATE_EST_I CRASH_DATE POSTED_SPEED_LIMIT TRAFFIC_CONTROL_DEVICE DEVICE_CONDITION WEATHER_CONDITION LIGHTING_CONDITION FIRST_CRASH_TYPE TRAFFICWAY_TYPE ... INJURIES_REPORTED_NOT_EVIDENT INJURIES_NO_INDICATION INJURIES_UNKNOWN CRASH_HOUR CRASH_DAY_OF_WEEK CRASH_MONTH LATITUDE LONGITUDE LOCATION geometry
0 04f52e7dd6cba4e59b164bc1b0a72914e02dcb72c1e8cb... NaN 11/16/2025 02:53:00 AM 25 NO CONTROLS NO CONTROLS CLEAR DARKNESS, LIGHTED ROAD PARKED MOTOR VEHICLE ALLEY ... 0.0 1.0 0.0 2 1 11 41.843939 -87.728338 POINT (-87.728337854378 41.843938811471) POINT (-87.72834 41.84394)
1 74a5da421beb4d046a7c1bf5a9264c83661d9b758846b3... NaN 11/16/2025 02:45:00 AM 30 NO CONTROLS NO CONTROLS CLEAR DARKNESS, LIGHTED ROAD SIDESWIPE SAME DIRECTION DIVIDED - W/MEDIAN (NOT RAISED) ... 0.0 2.0 0.0 2 1 11 41.903590 -87.659213 POINT (-87.659213209195 41.903590314092) POINT (-87.65921 41.90359)
2 469e9b3f61e06160cedc3fab2f1c6b0474d5740081b9ad... NaN 11/16/2025 02:30:00 AM 30 UNKNOWN UNKNOWN CLEAR DARKNESS SIDESWIPE SAME DIRECTION UNKNOWN INTERSECTION TYPE ... 0.0 2.0 0.0 2 1 11 41.895049 -87.745956 POINT (-87.745955516269 41.895049037964) POINT (-87.74596 41.89505)
3 9e69f2b053b40e3044d61cf2edb96e8a4a187d27cdde17... NaN 11/16/2025 01:30:00 AM 30 NO CONTROLS NO CONTROLS CLEAR DARKNESS, LIGHTED ROAD PARKED MOTOR VEHICLE ONE-WAY ... 0.0 1.0 0.0 1 1 11 41.657918 -87.596296 POINT (-87.596296309543 41.657918181213) POINT (-87.5963 41.65792)
4 e736b23d346f4b4c5c96f35388f58a8c0cda11d0a955a3... NaN 11/16/2025 01:30:00 AM 30 TRAFFIC SIGNAL FUNCTIONING PROPERLY CLEAR DARKNESS, LIGHTED ROAD TURNING FOUR WAY ... 0.0 2.0 0.0 1 1 11 41.884352 -87.656788 POINT (-87.656787588102 41.884351914473) POINT (-87.65679 41.88435)

5 rows × 49 columns

In [4]:
# Drop rows with missing or invalid coordinates
crashes = crashes.dropna(subset=["LATITUDE", "LONGITUDE"])

# Remove rows where lat/long are 0 or non-numeric 
crashes = crashes[
    (crashes["LATITUDE"] != 0) &
    (crashes["LONGITUDE"] != 0)
]
crashes = crashes[crashes["CRASH_DATE"].str.startswith("2024")]

# Convert to GeoDataFrame ( datum is crucial for my next steps )
crash_gdf = gpd.GeoDataFrame(
    crashes,
    geometry=gpd.points_from_xy(crashes["LONGITUDE"], crashes["LATITUDE"]),
    crs="EPSG:4326"
)
In [5]:
#lets see the colums so that I can see the colum name clearly. 
print(crashes.columns)

Index(['CRASH_RECORD_ID', 'CRASH_DATE_EST_I', 'CRASH_DATE',
       'POSTED_SPEED_LIMIT', 'TRAFFIC_CONTROL_DEVICE', 'DEVICE_CONDITION',
       'WEATHER_CONDITION', 'LIGHTING_CONDITION', 'FIRST_CRASH_TYPE',
       'TRAFFICWAY_TYPE', 'LANE_CNT', 'ALIGNMENT', 'ROADWAY_SURFACE_COND',
       'ROAD_DEFECT', 'REPORT_TYPE', 'CRASH_TYPE', 'INTERSECTION_RELATED_I',
       'NOT_RIGHT_OF_WAY_I', 'HIT_AND_RUN_I', 'DAMAGE', 'DATE_POLICE_NOTIFIED',
       'PRIM_CONTRIBUTORY_CAUSE', 'SEC_CONTRIBUTORY_CAUSE', 'STREET_NO',
       'STREET_DIRECTION', 'STREET_NAME', 'BEAT_OF_OCCURRENCE',
       'PHOTOS_TAKEN_I', 'STATEMENTS_TAKEN_I', 'DOORING_I', 'WORK_ZONE_I',
       'WORK_ZONE_TYPE', 'WORKERS_PRESENT_I', 'NUM_UNITS',
       'MOST_SEVERE_INJURY', 'INJURIES_TOTAL', 'INJURIES_FATAL',
       'INJURIES_INCAPACITATING', 'INJURIES_NON_INCAPACITATING',
       'INJURIES_REPORTED_NOT_EVIDENT', 'INJURIES_NO_INDICATION',
       'INJURIES_UNKNOWN', 'CRASH_HOUR', 'CRASH_DAY_OF_WEEK', 'CRASH_MONTH',
       'LATITUDE', 'LONGITUDE', 'LOCATION'],
      dtype='object')
In [6]:
crashes = pd.read_csv("Traffic_Crashes_20251115.csv", low_memory=False)
In [7]:
#Lets do Download Chicago street network

import osmnx as ox

# Lets add the drivable street network for Chicago
G = ox.graph_from_place("Chicago, Illinois, USA", network_type="drive")
edges = ox.graph_to_gdfs(G, nodes=False, edges=True)

edges.head()
Out[7]:
osmid highway oneway reversed length geometry lanes maxspeed name ref bridge access tunnel width junction area
u v key
702090 261263104 0 24536463 motorway_link True False 323.562424 LINESTRING (-87.81782 41.98343, -87.81813 41.9... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1223297118 0 [1000818941, 8220086] motorway True False 713.144140 LINESTRING (-87.81782 41.98343, -87.81879 41.9... 4 55 mph Kennedy Expressway I 90 NaN NaN NaN NaN NaN NaN
25779161 739968328 0 4378009 motorway True False 376.541227 LINESTRING (-87.661 41.90109, -87.66088 41.900... 4 45 mph Kennedy Expressway I 90;I 94 NaN NaN NaN NaN NaN NaN
25779173 709393011 0 [24229922, 56716748, 56716749] motorway_link True False 676.727456 LINESTRING (-87.65698 41.89495, -87.65687 41.8... 2 NaN NaN NaN yes NaN NaN NaN NaN NaN
25779174 0 906195924 motorway True False 110.038748 LINESTRING (-87.65698 41.89495, -87.65651 41.8... 4 45 mph Kennedy Expressway I 90;I 94 NaN NaN NaN NaN NaN NaN
In [8]:
pip install shapely==1.8.5.post1

Collecting shapely==1.8.5.post1
  Using cached Shapely-1.8.5.post1-cp310-cp310-macosx_10_9_x86_64.whl.metadata (43 kB)
Using cached Shapely-1.8.5.post1-cp310-cp310-macosx_10_9_x86_64.whl (1.2 MB)
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
geopandas 1.1.1 requires shapely>=2.0.0, but you have shapely 1.8.5.post1 which is incompatible.
osmnx 2.0.6 requires shapely>=2.0, but you have shapely 1.8.5.post1 which is incompatible.
prettymaps 1.0.0 requires osmnx==1.2.2, but you have osmnx 2.0.6 which is incompatible.
Successfully installed shapely-1.8.5.post1
Note: you may need to restart the kernel to use updated packages.
In [9]:
!pip install --upgrade geopandas osmnx shapely

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Using cached shapely-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl (1.8 MB)
Installing collected packages: shapely
  Attempting uninstall: shapely
    Found existing installation: Shapely 1.8.5.post1
    Uninstalling Shapely-1.8.5.post1:
      Successfully uninstalled Shapely-1.8.5.post1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
prettymaps 1.0.0 requires osmnx==1.2.2, but you have osmnx 2.0.6 which is incompatible.
prettymaps 1.0.0 requires Shapely<2.0,>=1.8, but you have shapely 2.1.2 which is incompatible.
Successfully installed shapely-2.1.2
In [10]:
import pandas as pd
import geopandas as gpd
import osmnx as ox
import folium
import branca.colormap as cm
from collections import Counter
In [11]:
# Adding and cleaning crash data
crashes = pd.read_csv("Traffic_Crashes_20251115.csv", low_memory=False)
crashes = crashes.dropna(subset=["LATITUDE", "LONGITUDE"])
crashes = crashes[
    (crashes["LATITUDE"].between(41.6, 42.1)) &
    (crashes["LONGITUDE"].between(-88.0, -87.5))
]

crash_gdf = gpd.GeoDataFrame(
    crashes,
    geometry=gpd.points_from_xy(crashes["LONGITUDE"], crashes["LATITUDE"]),
    crs="EPSG:4326"
)
In [12]:
# loading Chicago road network and clip to central area
G = ox.graph_from_place("Chicago, Illinois, USA", network_type="drive")
edges = ox.graph_to_gdfs(G, nodes=False, edges=True)

# Cliping manually to central Chicago
edges = edges.cx[-87.75:-87.60, 41.80:41.95]
In [13]:
# Reprojecting and match crashes to nearest edges
crash_gdf = crash_gdf.to_crs(edges.crs)
edges = edges.to_crs(edges.crs)

x = crash_gdf.geometry.x.values
y = crash_gdf.geometry.y.values
nearest_edges = ox.distance.nearest_edges(G, x, y)

edge_counts = Counter(nearest_edges)
edges["crash_count"] = edges.index.map(lambda idx: edge_counts.get(idx, 0))
In [14]:
# Filtering edges with crashes otherwise big data will impact my speed and taking lot of time to complete one run
edges = edges[edges["crash_count"] > 0]
In [15]:
print("Edges shape:", edges.shape)
print("Crash count column exists:", "crash_count" in edges.columns)
print("Crash count summary:\n", edges["crash_count"].describe() if "crash_count" in edges.columns else "No crash_count")

Edges shape: (22156, 17)
Crash count column exists: True
Crash count summary:
 count    22156.000000
mean        21.070139
std         35.000014
min          1.000000
25%          3.000000
50%          9.000000
75%         23.000000
max       1037.000000
Name: crash_count, dtype: float64
In [16]:
#lets check the length of accident data
len(edges)
edges.plot()
Out[16]:
<AxesSubplot: >
In [17]:
# After matching crashes to edges and calculating crash_count
edges["crash_density"] = edges["crash_count"]  # or any normalized field

# Lets do save to GeoJSON
edges.to_file("chicago_crash_roads.geojson", driver="GeoJSON")
In [18]:
gdf = gpd.read_file("chicago_crash_roads.geojson")

Skipping field highway: unsupported OGR type: 5
Skipping field lanes: unsupported OGR type: 5
Skipping field maxspeed: unsupported OGR type: 5
Skipping field name: unsupported OGR type: 5
Skipping field bridge: unsupported OGR type: 5
Skipping field tunnel: unsupported OGR type: 5
In [19]:
# lets see the crash density column for finding better scale 
print("Minimum accidents:", gdf["crash_density"].min())
print("Maximum accidents:", gdf["crash_density"].max())

# Or lets get a quick summary
print(gdf["crash_density"].describe())

Minimum accidents: 1
Maximum accidents: 1037
count    22156.000000
mean        21.070139
std         35.000014
min          1.000000
25%          3.000000
50%          9.000000
75%         23.000000
max       1037.000000
Name: crash_density, dtype: float64
In [20]:
# Now I here min accident is 1 and max is 1037 so I am going to keep 400 low and 1037 for high in hotsopt
# loading and clean GeoJSON
gdf = gpd.read_file("chicago_crash_roads.geojson")
gdf = gdf.drop(columns=["highway", "lanes", "maxspeed", "name", "bridge", "tunnel"], errors="ignore")
gdf["crash_density"] = gdf["crash_density"].fillna(0)

# lets clip to central Chicago
gdf = gdf.cx[-87.75:-87.60, 41.80:41.95]

#  Defining colormap for roads (green → red, 10 steps, scale 100–1037)
road_colormap = cm.LinearColormap(
    colors=list(reversed(cm.linear.RdYlGn_11.colors)),
    vmin=100,
    vmax=1037
).to_step(10)

# defineing colormap for hotspots (orange → dark red, ≥400 accidents)
hotspot_colormap = cm.LinearColormap(
    colors=["yellow", "red", "darkred"],
    vmin=400,
    vmax=1037
)

# center map Cartography
minx, miny, maxx, maxy = gdf.total_bounds
center = [(miny + maxy) / 2, (minx + maxx) / 2]

m = folium.Map(location=center, zoom_start=13, tiles="CartoDB positron")

# road segments styled by crash density
folium.GeoJson(
    gdf,
    style_function=lambda feature: {
        "color": road_colormap(feature["properties"]["crash_density"]),
        "weight": 2 + (feature["properties"]["crash_density"] / 150),
        "opacity": 0.9
    }
).add_to(m)

# hotspot markers (≥400 accidents)
hotspots = gdf[gdf["crash_density"] >= 400]
for _, row in hotspots.iterrows():
    centroid = row.geometry.centroid
    folium.CircleMarker(
        location=[centroid.y, centroid.x],
        radius=6,
        color=hotspot_colormap(row["crash_density"]),
        fill=True,
        fill_color=hotspot_colormap(row["crash_density"]),
        fill_opacity=0.8,
        popup=f"Hotspot: {row['crash_density']} accidents"
    ).add_to(m)

#  legends (both on right side)
road_colormap.caption = "Road crash density (100–1037)"
road_colormap.add_to(m)

hotspot_colormap.caption = "Hotspot density (yellow → dark red)"
hotspot_colormap.add_to(m)

#  heading 
title_html = """
<h1 align="center" style="font-size:28px; color:#2C3E50;">
<b>Chicago Traffic Accident Hotspots</b>
</h1>
"""
m.get_root().html.add_child(folium.Element(title_html))

# lets make it fit bounds and save
m.fit_bounds([[miny, minx], [maxy, maxx]])
m.save("chicago_accidents_hotspots_2025.html")


m

Skipping field highway: unsupported OGR type: 5
Skipping field lanes: unsupported OGR type: 5
Skipping field maxspeed: unsupported OGR type: 5
Skipping field name: unsupported OGR type: 5
Skipping field bridge: unsupported OGR type: 5
Skipping field tunnel: unsupported OGR type: 5
Out[20]:
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